library(tidyverse)
library(DT)
library(lubridate)

  1. Load the data into R.

  2. Descriptive Analysis
    2-1.
    2-2.
    2-3.
    and more..

  3. Determine the optimal number of clusters using methods like the Elbow method.

  4. Perform K-means clustering.

  5. Analyze the resulting clusters to interpret different groupings of orders based on acknowledgment times and other relevant factors.

  6. Load the data into R.

I’ve loaded already. Let’s take a look at the dataset to get a sense of what we’re working with.

order_late %>%
  dplyr::mutate(order_date = as.Date(order_date),
                delivery_date = as.Date(delivery_date),
                ship_date = as.Date(ship_date),
                date_acknowledge = as.Date(date_acknowledge),
                date_acknowledgement_calc = as.Date(date_acknowledgement_calc)) %>% 
  DT::datatable(options = list(scrollX = TRUE))

Next things to do: make md. data description.